We propose a fully automatic system for cardiac view classification of echocardiogram. Given an echo study video sequence, the system outputs a view label among the pre-defined standard views. The system is built based on a machine learning approach that extracts knowledge from an annotated database. It characterizes three features: 1) integrating local and global evidence, 2) utilizing view specific knowledge, and 3) employing a multi-class Logit-boost algorithm. In our prototype system, we classify four standard cardiac views: apical four chamber and apical two chamber, parasternal long axis and parasternal short axis (at mid cavity). We achieve a classification accuracy over 96% both of training and test data sets and the system runs in a second in the environment of Pentium 4 PC with 3.4 GHz CPU and 1.5 G RAM.

We propose a probabilistic, hierarchical, and discriminant (PHD) framework for fast and accurate detection of deformable anatomic structures from medical images. The PHD framework has three characteristics. First, it integrates distinctive primitives of the anatomic structures at global, segmental, and landmark levels in a probabilistic manner. Second, since the configuration of the anatomic structures lies in a high-dimensional parameter space, it seeks the best configuration via a hierarchical evaluation of the detection probability that quickly prunes the search space. Finally, to separate the primitive from the background, it adopts a discriminative boosting learning implementation. We apply the PHD framework for accurately detecting various deformable anatomic structures from M- mode and Doppler echocardiograms in about a second.

We tackle the problem of automatically classifying cardiac view for an echocardiographic sequence as a multiclass object detection. As a solution, we present an imagebased multiclass boosting procedure. In contrast with conventional approaches for multiple object detection that train multiple binary classifiers, one per object, we learn only one multiclass classifier using the LogitBoosting algorithm. To utilize the fact that, in the midst of boosting, one class is fully separated from the remaining classes, we propose to learn a tree structure that focuses on the remaining classes to improve learning efficiency. Further, we accommodate the large number of background images using a cascade of boosted multiclass classifiers, which is able to simultaneously detect and classify multiple objects while rejecting the background class quickly. Our experiments on echocardiographic view classification demonstrate promising performances of image-based multiclass boosting.